All computer primitives are relatively straightforward in pure form and vastly more complicated in real-world scenarios.
RAG Is Simpler Than You Think
191–197 of 197 posts
Re: RAG Is Simpler Than You Think
#192I worked on large scale RAG systems before and can say people vastly underestimate full text search and vastly overestimate embeddings. FTS is really easy, portable and scalable and gets you very far, the 80/20 rule applies. Embeddings appear to be nice and magic but when you really get into them you notice: semantic similarity isn’t as good as you think and certainly it won’t make everyone happy. You will inevitably…
[deleted]
Re: RAG Is Simpler Than You Think
#193Earlier quoted context omitted.
That could work in a way, but it's very expensive as expressed and I do not know of prominent robust implementations. On the other hand, your post may contain a good idea: L=instruct_LLM("provide a list of synonyms and periphrases of terms T within context C", T, C); then iter(`grep l in L`). One NN query and a `grep` collection. But again, if one wanted to order the results, it is either through a dumb crierion or t…
You do not know of prominent robust implementations? This is how Claude Code, GPT Codex, etc have worked for a couple years. And they do tend to be impressively good at navigating large amounts of text.
Thank you, no, I did not know that. Where have you found the info? Sebastian Raschka, Anthropic/OpenAI blogs?
(BTW: you made me realize - I had to take "time off" for over half a year... I am sure I missed a lot.)
--
Edit: for clarity: for "full text search" we remain on the interpretation of "searching for literal substrings" - and whether plain user provided keywords list or LLM enriched list based on the former, and whether more or less successful, it remains a syntactic search quite distinct from a semantic one. Having an LLM enrich the original keywords list can be a good idea, but the possibility of misses remains when compared to a properly working semantic search.
Re: RAG Is Simpler Than You Think
#194Earlier quoted context omitted.
> people vastly underestimate full text search It is not psychological, it is fully justified: substring search cannot find synonyms, periphrases and mistaken neighbours.
> It is not psychological, it is fully justified: substring search cannot find synonyms, periphrases and mistaken neighbours. It is, if people don't even stop to think if they need synonyms, periphrases, or mistaken neighbours. As the blog post points out, more often than not you don't, particularly if your primary usecase is to search for technical keywords or codenames.
[deleted]
Re: RAG Is Simpler Than You Think
#195I worked on large scale RAG systems before and can say people vastly underestimate full text search and vastly overestimate embeddings. FTS is really easy, portable and scalable and gets you very far, the 80/20 rule applies. Embeddings appear to be nice and magic but when you really get into them you notice: semantic similarity isn’t as good as you think and certainly it won’t make everyone happy. You will inevitably…
[deleted]
Re: RAG Is Simpler Than You Think
#196Helpful framing, thanks for sharing!
Re: RAG Is Simpler Than You Think
#197Earlier quoted context omitted.
You do not know of prominent robust implementations? This is how Claude Code, GPT Codex, etc have worked for a couple years. And they do tend to be impressively good at navigating large amounts of text.
Thank you, no, I did not know that. Where have you found the info? Sebastian Raschka, Anthropic/OpenAI blogs? (BTW: you made me realize - I had to take "time off" for over half a year... I am sure I missed a lot.) -- Edit: for clarity: for "full text search" we remain on the interpretation of "searching for literal substrings" - and whether plain user provided keywords list or LLM enriched list based on the former, a…
Yes, they navigate with heavy use of the "grep" tool